Pattern recognition is becoming increasingly significant in the era of electronic data processing. Its range of use extends from automation technology to machine-based image and text processing, where it is used for automatic letter distribution (address reading) or to evaluate formulas or documents. The objective of pattern recognition is to allocate to a piece of electronically pre-processed image information an identification that reliably coincides with the true meaning of the pattern. Statistics-based pattern-recognition methods assess a digitized piece of image information with estimates from which the degree of association of the pattern with a class of patterns can be read. With K given target classes, the class whose estimation result corresponds to the maximum of all K estimates is generally awarded this assessment. A recognition system is more reliable the more frequently the target class estimated as the maximum class matches the true target class (meaning). A network classifier used to this point, which comprises a complete ensemble of two-class classifiers and has the task of discriminating the K target classes, is based on the fact that a two-class classifier is calculated for all possible K*(K-1)/2 class pairs. During a reading operation, for the present pattern, each of the two-class classifiers supplies an estimate of the association of the pattern with one of the two fundamental target classes. The result is K*(K-1)/2 estimates, which are not independent among themselves. From these K*(K-1)/2 estimates, K estimates are to be formed, one for each target class. The theory provides a mathematical rule for this relationship, which is described in Wojciech W. Siedlecki, A formula for multiclass distributed classifiers, Pattern Recognition Letters 15 (1994). The practice of classifiers demonstrates that the applicability of this rule is unsatisfactory, because the two-class classifiers supply no statistical conclusion probabilities as soon as they estimate a foreign pattern that is not part of their adapted class range. In practice, this means that shutoff mechanisms must deactivate those classifiers that are not responsible for the pattern as early as possible. The shutoff rules used to this point in practice are largely of a heuristic nature. Consequently, an element of arbitrariness that is not statistically controlled is factored into the processing of network classifiers. This rule-based iteration of variables that experience a measurable statistic behavior significantly worsens the recognition results. Rule-based iteration of network classifiers additionally prevents the possibility of effectively re-training the classifier system when the samples are changed. With 30 or more classes to be discriminated, the use of network classifiers also meets with fundamental problems:
1. The number of components (pair classifiers) to be stored increases quadratically with the number of classes (K* (K-1)/2). PA0 2. An assessment and combination of the component-related estimates into a reliable total estimate becomes increasingly less reliable with a growing number of classes. PA0 3. Adaptations of a network classifier to country-specific writing styles incur considerable costs in the adaptation phase. PA0 2. A Cartesian product vector is also formed in a second embodiment. By means of a subspace transformation U, this vector is converted into a shortened vector, of which only the most crucial components corresponding to the eigenvalue distribution of the transformation matrix U are used to adapt a quadratic classifier. This quadratic classifier then maps the transformed and reduced vector for an estimation vector onto the target class. PA0 3. Fundamentally, in another embodiment a meta-class classifier, which is trained over groups of class quantities, generates estimates for the groups prior to activation of the respective selection of the two- or multi-class classifiers. Afterward, the two- or multi-class classifiers for the characters of the groups whose estimated value lies above an established threshold are activated. To determine the total estimate, the group estimates are linked to the estimates of the respective, associated character-assessment classifiers for the character target classes according to a unified rule such that the sum of all character estimates obtained in this manner yields a number that can be normalized to 1. The first variation yields the most precise results with the most computational effort, while the second and third variations contribute to the reduction in the computational effort.